Energy efficient power cap configurations through Pareto front analysis and machine learning categorization

• Published in: Cluster computing 2024-06, Vol.27 (3), p.3433-3449
• Main Authors: Cabrera, Alberto, Almeida, Francisco, Castellanos-Nieves, Dagoberto, Oleksiak, Ariel, Blanco, Vicente
• Format: Article
• Language: English
• Subjects: Computer Communication Networks; Computer Science; Operating Systems; Processor Architectures
• ISSN: 1386-7857; 1573-7543
• DOI: 10.1007/s10586-023-04151-2

The growing demand for more computing resources has increased the overall energy consumption of computer systems. To support this increasing demand, power and energy consumption must be considered as a constraint on software execution. Modern architectures provide tools for managing the power constraints of a system directly. The Intel Power Cap is a relatively new tool developed to give users fine-grained control over power usage at the central processing unit (CPU) level. The complexity of these tools, in addition to the high variety of modern heterogeneous architectures, hinders predictions of the energy consumption and the performance of any target software. The application of power capping technologies usually leads to the bi-objective optimization problem for energy efficiency and execution time but optimal power constraints could also produce exceeding performance losses. Thus, methods and tools are needed to calculate the proper parameters for power capping technologies, and to optimize energy efficiency. We propose a methodology to analyze the performance and the energy efficiency trade-offs using this power cap technology for a given application. A Pareto front is extracted for the multi-objective performance and energy problem, which represents multiple feasible configurations for both objectives. An extensive experimentation is carried out to categorize the different applications to determine the overall optimal power cap configurations. We propose the use of machine learning (ML) clustering techniques to categorize each application in the target architecture. The use of ML allows us to automate the process and simplifies the effort required to solve the optimization problem. A practical case is presented where we categorize the applications using ML techniques, with the possibility of adding a new application into an existing categorization.